A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

• Published in: Environments (Basel, Switzerland) Vol. 9; no. 7; p. 85
• Main Authors: Khudhair, Zahraa S., Zubaidi, Salah L., Ortega-Martorell, Sandra, Al-Ansari, Nadhir, Ethaib, Saleem, Hashim, Khalid
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2022
• Subjects: Accuracy; Algorithms; Artificial intelligence; Chemical oxygen demand; Data processing; Dissolved oxygen; Forecasting; Freshwater resources; Geoteknik; hybrid model; Internet of Things; Machine learning; Mathematical models; metaheuristic algorithms; Model accuracy; Modelling; Neural networks; Parameters; Prediction models; Rain; Rivers; Soft computing; Soil Mechanics; Support vector machines; Time series; Water quality; water quality parameters
• ISSN: 2076-3298; 2076-3298
• DOI: 10.3390/environments9070085

Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.